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On the impact of conditional expectation estimators in portfolio theory

Author

Listed:
  • Sergio Ortobelli

    (University of Bergamo
    VSB-TU Ostrava)

  • Noureddine Kouaissah

    (University of Bergamo
    VSB-TU Ostrava)

  • Tomáš Tichý

    (VSB-TU Ostrava)

Abstract

In this paper, we investigate the implications for portfolio theory of using conditional expectation estimators. First, we focus on the approximation of the conditional expectation within large-scale portfolio selection problems. In this context, we propose a new consistent multivariate kernel estimator to approximate the conditional expectation and it optimizes the bandwidth selection of kernel-type estimators. Second, we deal with the portfolio selection problem from the point of view of different non-satiable investors, namely risk-averse and risk-seeker investors. In particular, using a well-known ordering classification, we first identify different definitions of returns based on the investors preferences. Finally, for each problem, we examine several admissible portfolio optimization problems applied to the US stock market. The proposed empirical analysis allows us to evaluate the impact of the conditional expectation estimators in portfolio theory.

Suggested Citation

  • Sergio Ortobelli & Noureddine Kouaissah & Tomáš Tichý, 2017. "On the impact of conditional expectation estimators in portfolio theory," Computational Management Science, Springer, vol. 14(4), pages 535-557, October.
    • Handle: RePEc:spr:comgts:v:14:y:2017:i:4:d:10.1007_s10287-017-0282-9
    DOI: 10.1007/s10287-017-0282-9
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    Cited by:

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    6. D'Amico, Guglielmo & De Blasis, Riccardo, 2024. "Dividend based risk measures: A Markov chain approach," Applied Mathematics and Computation, Elsevier, vol. 471(C).

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